{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "90023b29-6e4d-4654-9c3d-8f8b0e5f0fca",
   "metadata": {},
   "source": [
    "## B01_Descriptive Analysis and Dashboarding\n",
    "\n",
    "This code implements exploratory data analysis and interactive dashboards."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c67ec235-bb44-43bb-b6aa-32e3c6887b68",
   "metadata": {},
   "source": [
    "### 1. init spark session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bd7ad00e-1b59-4dc1-897e-97074b25a883",
   "metadata": {},
   "outputs": [],
   "source": [
    "import findspark\n",
    "findspark.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4e3ce44a-50dc-42b0-a116-edba2a567ae9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql import SparkSession\n",
    "spark = SparkSession.builder.appName(\"unemployment data\").getOrCreate()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c417c08c-b6d6-4c1f-b4a8-abdb1b05de2d",
   "metadata": {},
   "source": [
    "### 2. read the parquet file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bcd1f0e3-b6eb-4f2b-aa9f-309b342e26cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----+----------+--------------+----------------+---+--------------------+-------------+------+-----+---+\n",
      "| Any|Codi_Barri|Codi_Districte|Demanda_ocupacio|Mes|           Nom_Barri|Nom_Districte|Nombre| Sexe|_id|\n",
      "+----+----------+--------------+----------------+---+--------------------+-------------+------+-----+---+\n",
      "|2016|         1|             1|  atur registrat|  1|            el Raval| Ciutat Vella|  2431|Homes|  1|\n",
      "|2016|         2|             1|  atur registrat|  1|      el Barri Gòtic| Ciutat Vella|   588|Homes|  2|\n",
      "|2016|         3|             1|  atur registrat|  1|      la Barceloneta| Ciutat Vella|   637|Homes|  3|\n",
      "|2016|         4|             1|  atur registrat|  1|Sant Pere, Santa ...| Ciutat Vella|   878|Homes|  4|\n",
      "|2016|         5|             2|  atur registrat|  1|       el Fort Pienc|     Eixample|   693|Homes|  5|\n",
      "|2016|         6|             2|  atur registrat|  1|  la Sagrada Família|     Eixample|  1154|Homes|  6|\n",
      "|2016|         7|             2|  atur registrat|  1|la Dreta de l'Eix...|     Eixample|   772|Homes|  7|\n",
      "|2016|         8|             2|  atur registrat|  1|l'Antiga Esquerra...|     Eixample|   855|Homes|  8|\n",
      "|2016|         9|             2|  atur registrat|  1|la Nova Esquerra ...|     Eixample|  1255|Homes|  9|\n",
      "|2016|        10|             2|  atur registrat|  1|         Sant Antoni|     Eixample|   922|Homes| 10|\n",
      "+----+----------+--------------+----------------+---+--------------------+-------------+------+-----+---+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "file_path = \"./Exploitation Zone/unemployment.parquet\"\n",
    "\n",
    "df = spark.read.parquet(file_path)\n",
    "\n",
    "df.show(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6f7bd771-37d0-40e1-9949-4654f03689f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- Any: integer (nullable = true)\n",
      " |-- Codi_Barri: integer (nullable = true)\n",
      " |-- Codi_Districte: integer (nullable = true)\n",
      " |-- Demanda_ocupacio: string (nullable = true)\n",
      " |-- Mes: integer (nullable = true)\n",
      " |-- Nom_Barri: string (nullable = true)\n",
      " |-- Nom_Districte: string (nullable = true)\n",
      " |-- Nombre: integer (nullable = true)\n",
      " |-- Sexe: string (nullable = true)\n",
      " |-- _id: long (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a57a45c6-ee7b-431f-8cde-b983c335edd4",
   "metadata": {},
   "source": [
    "### 3. Analysis: Gender Unemployment Rate Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8c75023e-8dbe-4708-a49e-f8371ba24f5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "gender_unemployment = (\n",
    "    df.groupBy(\"Sexe\")\n",
    "    .sum(\"Nombre\")\n",
    "    .withColumnRenamed(\"sum(Nombre)\", \"Total_Unemployment\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "811fe840-5b28-4a4d-ab40-2695ccc8d995",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----+------------------+\n",
      "| Sexe|Total_Unemployment|\n",
      "+-----+------------------+\n",
      "|Dones|            252618|\n",
      "|Homes|            426889|\n",
      "+-----+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gender_unemployment.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "927594fa-881a-4bc6-8dda-ef95b9d8b47c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5368ca09-aa56-48f1-ba7e-708364098710",
   "metadata": {},
   "outputs": [],
   "source": [
    "gender_unemployment_pd = gender_unemployment.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "edfa657c-ba27-4156-bb76-c4a8c70404f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(gender_unemployment_pd['Sexe'], gender_unemployment_pd['Total_Unemployment'], color=['blue', 'orange'])\n",
    "plt.xlabel('Gender')\n",
    "plt.ylabel('Total Unemployment')\n",
    "plt.title('Gender Unemployment Rate Comparison')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "658e7a61-1bbf-46ff-b5e7-9f5233d2c33d",
   "metadata": {},
   "source": [
    "### 4. Analysis: Unemployment Rate by District"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f032a5fa-8d47-436a-8715-c161081cbf66",
   "metadata": {},
   "outputs": [],
   "source": [
    "district_unemployment = df.groupBy(\"Nom_Districte\").sum(\"Nombre\").withColumnRenamed(\"sum(Nombre)\", \"Total_Unemployment\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "96600f3c-167c-448a-a2a4-aecdc9bdb25c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------------+------------------+\n",
      "|      Nom_Districte|Total_Unemployment|\n",
      "+-------------------+------------------+\n",
      "|             Gràcia|             33682|\n",
      "|         Sant Martí|             86717|\n",
      "|     Horta-Guinardó|             56395|\n",
      "|          Les Corts|             34962|\n",
      "|     Sants-Montjuïc|            106328|\n",
      "|         Nou Barris|             72267|\n",
      "|Sarrià-Sant Gervasi|             32877|\n",
      "|           Eixample|            123307|\n",
      "|          No consta|               243|\n",
      "|        Sant Andreu|             51216|\n",
      "|       Ciutat Vella|             81513|\n",
      "+-------------------+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "district_unemployment.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a4c4b099-4ebb-4fa4-8a5b-96a8a9468ef1",
   "metadata": {},
   "outputs": [],
   "source": [
    "district_unemployment_pd = district_unemployment.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c718e79d-2b6b-44f7-9dc0-46ea648cc59a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(district_unemployment_pd['Nom_Districte'], district_unemployment_pd['Total_Unemployment'], color='skyblue')\n",
    "plt.xlabel('District')\n",
    "plt.ylabel('Total Unemployment')\n",
    "plt.title('District Unemployment Rate Comparison')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2775a16-27f4-4355-b185-e82f44c15fa0",
   "metadata": {},
   "source": [
    "### 5. Analysis: Unemployment Rate Time Trend Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b6ee6f68-5e24-4fc0-acce-a04fb72b1f78",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql.functions import concat_ws"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "863ca4eb-e29a-48cb-b64f-451b41a55fcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----+------------------+\n",
      "| Any|Total_Unemployment|\n",
      "+----+------------------+\n",
      "|2018|             38864|\n",
      "|2023|             39045|\n",
      "|2022|             40049|\n",
      "|2013|             81967|\n",
      "|2014|             77572|\n",
      "|2019|             36296|\n",
      "|2020|             36211|\n",
      "|2012|             81330|\n",
      "|2016|             47844|\n",
      "|2011|             76539|\n",
      "|2017|             41721|\n",
      "|2021|             82069|\n",
      "+----+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "time_unemployment = df.groupBy(\"Any\").sum(\"Nombre\").withColumnRenamed(\"sum(Nombre)\", \"Total_Unemployment\")\n",
    "time_unemployment.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3b931ba1-418b-48ed-9433-79418fd06a0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_unemployment_pd = time_unemployment.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "66c9b0ff-1e61-48ad-a96b-3edb03a5bc51",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3465c657-2b65-4331-95ef-b7ed52ad9a87",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_unemployment_pd['Any'] = pd.to_datetime(time_unemployment_pd['Any'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e958bde7-4895-4086-b788-f84c185cfaba",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_unemployment_pd = time_unemployment_pd.sort_values(by='Any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "79f228e1-f1ce-40bf-88cf-90755703e9a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(time_unemployment_pd['Any'], time_unemployment_pd['Total_Unemployment'], marker='o', linestyle='-', color='b')\n",
    "plt.xlabel('Time (Any)')\n",
    "plt.ylabel('Total Unemployment')\n",
    "plt.title('Unemployment Rate Time Trend')\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef6542c7-2e0e-4014-bfc1-6866e0829e83",
   "metadata": {},
   "source": [
    "### 6. interactive dashboards\n",
    "\n",
    "- using `pip install ipywidgets` to install lib\n",
    "- using `pip install seaborn` to install lib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15c000e3-dd86-49dc-b6db-050485d916f2",
   "metadata": {},
   "source": [
    "#### 6.1 get datas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1705b2b2-8ad2-4b67-b274-8aaba9cf8745",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "59c9cf89-2417-4d4f-a76c-482f4db7ff5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Any</th>\n",
       "      <th>Nom_Districte</th>\n",
       "      <th>Sexe</th>\n",
       "      <th>Codi_Barri</th>\n",
       "      <th>Codi_Districte</th>\n",
       "      <th>Demanda_ocupacio</th>\n",
       "      <th>Mes</th>\n",
       "      <th>Nom_Barri</th>\n",
       "      <th>Nombre</th>\n",
       "      <th>_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>Dones</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>atur registratatur registratatur registratatur...</td>\n",
       "      <td>4</td>\n",
       "      <td>el Ravalel Barri Gòticla BarcelonetaSant Pere,...</td>\n",
       "      <td>3796</td>\n",
       "      <td>306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>Homes</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>atur registratatur registratatur registratatur...</td>\n",
       "      <td>4</td>\n",
       "      <td>el Ravalel Barri Gòticla BarcelonetaSant Pere,...</td>\n",
       "      <td>6192</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011</td>\n",
       "      <td>Eixample</td>\n",
       "      <td>Dones</td>\n",
       "      <td>45</td>\n",
       "      <td>12</td>\n",
       "      <td>atur registratatur registratatur registratatur...</td>\n",
       "      <td>6</td>\n",
       "      <td>el Fort Piencla Sagrada Famíliala Dreta de l'E...</td>\n",
       "      <td>7611</td>\n",
       "      <td>489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011</td>\n",
       "      <td>Eixample</td>\n",
       "      <td>Homes</td>\n",
       "      <td>45</td>\n",
       "      <td>12</td>\n",
       "      <td>atur registratatur registratatur registratatur...</td>\n",
       "      <td>6</td>\n",
       "      <td>el Fort Piencla Sagrada Famíliala Dreta de l'E...</td>\n",
       "      <td>7407</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011</td>\n",
       "      <td>Gràcia</td>\n",
       "      <td>Homes</td>\n",
       "      <td>150</td>\n",
       "      <td>30</td>\n",
       "      <td>atur registratatur registratatur registratatur...</td>\n",
       "      <td>5</td>\n",
       "      <td>Vallcarca i els Penitentsel Collla Salutla Vil...</td>\n",
       "      <td>3601</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Any Nom_Districte   Sexe  Codi_Barri  Codi_Districte  \\\n",
       "0  2011  Ciutat Vella  Dones          10               4   \n",
       "1  2011  Ciutat Vella  Homes          10               4   \n",
       "2  2011      Eixample  Dones          45              12   \n",
       "3  2011      Eixample  Homes          45              12   \n",
       "4  2011        Gràcia  Homes         150              30   \n",
       "\n",
       "                                    Demanda_ocupacio  Mes  \\\n",
       "0  atur registratatur registratatur registratatur...    4   \n",
       "1  atur registratatur registratatur registratatur...    4   \n",
       "2  atur registratatur registratatur registratatur...    6   \n",
       "3  atur registratatur registratatur registratatur...    6   \n",
       "4  atur registratatur registratatur registratatur...    5   \n",
       "\n",
       "                                           Nom_Barri  Nombre  _id  \n",
       "0  el Ravalel Barri Gòticla BarcelonetaSant Pere,...    3796  306  \n",
       "1  el Ravalel Barri Gòticla BarcelonetaSant Pere,...    6192   10  \n",
       "2  el Fort Piencla Sagrada Famíliala Dreta de l'E...    7611  489  \n",
       "3  el Fort Piencla Sagrada Famíliala Dreta de l'E...    7407   45  \n",
       "4  Vallcarca i els Penitentsel Collla Salutla Vil...    3601  150  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_grouped = data.groupby(['Any', 'Nom_Districte', 'Sexe']).sum().reset_index()\n",
    "data_grouped.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db02d10f-cb08-448c-8bf2-7e369fea6585",
   "metadata": {},
   "source": [
    "#### 6.2 plot function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "038a5dfc-975f-4b24-b4a3-f7440c916047",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9d3bf0d2-1c3e-4891-b6b8-7bd7600c5242",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_unemployment(district, gender, start_year, end_year):\n",
    "    # filter data\n",
    "    filtered_data = data_grouped[\n",
    "        (data_grouped['Nom_Districte'] == district) &\n",
    "        (data_grouped['Sexe'] == gender) &\n",
    "        (data_grouped['Any'] >= start_year) &\n",
    "        (data_grouped['Any'] <= end_year)\n",
    "    ]\n",
    "\n",
    "    # plot\n",
    "    sns.lineplot(data=filtered_data, x='Any', y='Nombre', marker='o')\n",
    "    plt.xlabel('Year')\n",
    "    plt.ylabel('Total Unemployment')\n",
    "    plt.title(f'Unemployment Rate Trend for {district} ({gender})')\n",
    "    plt.grid(True)\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "584e8371-3063-4310-b95a-0f85f19feb18",
   "metadata": {},
   "source": [
    "#### 6.3 get unique datas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e212ae0d-a633-41da-9215-03976f7b8048",
   "metadata": {},
   "outputs": [],
   "source": [
    "districts = data['Nom_Districte'].unique()\n",
    "genders = data['Sexe'].unique()\n",
    "years = data['Any'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "369605ff-aea3-47e1-ae82-7a1383b2aa14",
   "metadata": {},
   "source": [
    "#### 6.4 widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bce460b1-b0f1-49e3-a45e-effee498a249",
   "metadata": {},
   "outputs": [],
   "source": [
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact, interact_manual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ff5fd3a3-e85c-4c96-92fa-3a811bdf6d7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "district_widget = widgets.Dropdown(options=districts, description='District:')\n",
    "gender_widget = widgets.Dropdown(options=genders, description='Gender:')\n",
    "start_year_widget = widgets.IntSlider(value=years.min(), min=years.min(), max=years.max(), step=1, description='Start Year:')\n",
    "end_year_widget = widgets.IntSlider(value=years.max(), min=years.min(), max=years.max(), step=1, description='End Year:')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b4e12da-cb62-45d6-803c-d17c2ceba363",
   "metadata": {},
   "source": [
    "#### 6.5 create interactive dashboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7ec0b9e3-c649-4937-89a6-72a4b68b84c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3e24f1065fac40ceaa9e99e13b45009d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='District:', options=('Ciutat Vella', 'Eixample', 'Sants-Montjuïc',…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_unemployment(district, gender, start_year, end_year)>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interact(\n",
    "    plot_unemployment,\n",
    "    district=district_widget,\n",
    "    gender=gender_widget,\n",
    "    start_year=start_year_widget,\n",
    "    end_year=end_year_widget\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1421c284-67bf-4c14-aa8e-bbdf54ba348a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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